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Innovation Enigma for Banks

Each use case that brings Machine learning in play, gets drenched in data. The industry is now waking up to the real challenge of deploying Machine Learning based models in traditional rule-based applications. Machine learning models are data guzzling engines whose accuracy and performance depends on the data quality and continuity. Imagine them as high performance oriented F1 car engines that need clean & unadulterated fuel to perform at the best of their capacities.

And here lies the big challenge, many organizations over time, have become lax in the data capture & data management for their legacy applications. The trend notably worsened with the advent of data warehousing becoming popular in the early 2000s, as the big idea was to dump everything in the warehouse, flatten the schema to create a long single record, add a timestamp and then mine the data for information & insights.

This is now coming back to haunt many such projects. When the data quality is discussed, many managers complain about the sparse datasets, blank columns, or even junk data. Lack of discipline in maintaining data dictionaries also creates another problem of data relevance getting lost. In many cases, there are standard fields that were present just to support an off the shelf application’s analytics module to get structured results.

With time the knowledge of which of these fields are tool specific and which are the fields with actual data was lost, and the practice of lifting and dumping entire schemas continued. That leads us to where we are today, where everyone is afraid of taking a call to drop certain data sets which most people feel are irrelevant. The lack of courage to take a call and face a potential break in a working system seems to be too big for people to break the rules.

All this has caught banks in a catch 22 of sorts. So, what is the way ahead for us? Of course, you can take the radical approach one of the respected financial services futurist Chris Skinner says, get rid of legacy, legacy people, legacy mindset, legacy systems, even legacy customers. But is that so easy? He shares the approach to be a simple 2 step process, as confirmed to him by numerous bankers he discussed legacy issues with:

  1. Start with bite-sized transformations
  2. Create a rolling snowball effect

But is it this easy? Of course, senior management has to be courageous, to agree to the radical changes to the core systems, but is this push that easy to make? Many times, the story is deeper than it looks at the onset. Someone will have to take the risk of taking the unpopular decisions. Someone should be courageous enough to agree, let’s drop these data sets as we may not need these datasets in our fresh start.

In our quest of becoming data driven from processes driven we’re placing huge importance and value to the data, and that’s becoming part of the problem for decision making too. Only if we know that the core of the strategy i.e. data itself is faulty and garbage then we may have to devise a new strategy and many banks are lacking that creative intent to think out of the box.

There are certain banks that are taking the brave leap of faith and immersing them to the data sea and lakes with some filters that may clean the data in, but it will be a long time-consuming transformation, and we have to be patient as much as we have to be ambitious to achieve the audacious.

Exciting times ahead, indeed.

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Comments: (2)

Ketharaman Swaminathan
Ketharaman Swaminathan - GTM360 Marketing Solutions - Pune 05 February, 2019, 14:52Be the first to give this comment the thumbs up 0 likes

If AI / ML suffers from the same, good-old GIGO problem that has plagued BI, DW, DM, OLTP and other technologies that preceded it, how transformative / revolutionary / disruptive is AI / ML really? I've always wondered, why can't AI / ML drink some of its own learning and intelligence Kool-Aid and work with non-pristine data quality?

Shailendra Malik
Shailendra Malik - DBS Bank - Singapore 06 February, 2019, 01:34Be the first to give this comment the thumbs up 0 likes

Ohh yes it can, and it is. But, that is taking its own evolution curve. Auto data fixing is another stream where AI / ML are taking giant strides and more tools are coming up. Read through my other blog post on RPA from Nov-2018, that may help.

Shailendra Malik

Shailendra Malik

Vice President - IT Platform (Audit)

DBS Bank

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Singapore

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This post is from a series of posts in the group:

Banking Strategy, Digital and Transformation

Latest thinking in respect to Banking Strategy, Digital and Transformation. Harnessing our collective wisdom to make banking better. Ambrish Parmar


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